Evaluation of the Composition, Thermal and Mechanical Behavior, and Color Changes of Artificially and Naturally Aged Polymers for the Conservation of Stained Glass Windows.

Investigations of historical conservation materials on historical stained glass windows of the Naumburg Cathedral in Germany offered an opportunity for the study of polymers, naturally aged in a non-controlled environment. This allowed the conservation history of the cathedral to be traced and expanded by valuable insights. The historical materials were characterized through the use of spectroscopy (FTIR, Raman), thermal analysis, PY-GC/MS, and SEC on taken samples. The analyses show that acrylate resins were predominantly used for conservation. The lamination material from the 1940s is particularly noteworthy. Epoxy resins were also identified in isolated cases. Artificial aging was used to investigate the influence of environmental influences on the properties of the identified materials. Through a multi-stage aging program, influences of UV radiation, high temperatures and high humidity can be considered in isolation. Piaflex F20, Epilox, Paraloid B72 as a modern material and combinations of Paraloid B72/diisobutyl phthalate and PMA/diisobutyl phthalate were investigated. The parameters yellowing, FTIR spectra, Raman spectra, molecular mass and conformation, glass transition temperature, thermal behavior, and adhesive strength on glass were determined. The effects of the environmental parameters on the investigated materials are differentiated. UV and extreme temperatures tend to show a stronger influence than humidity. The comparison of the artificially aged samples with the naturally aged samples from the cathedral shows that the latter were less aged. Recommendations for the conservation of the historical stained glass windows were derived from the results of the investigation.

Agricultural application of microplastic-rich sewage sludge leads to further uncontrolled contamination.

Municipal sewage sludge has been shown to be high in microplastics (MP) and is applied to agricultural land as fertiliser in many countries. The authors recently proposed in a viewpoint article that MP applied to land in this way may well contaminate other areas in an uncontrolled way. This study examined experimental plots with known history of application of sewage sludge. Results showed that 44% of the MP load found on sludge-applied land was found on nearby land never directly applied with sludge. Examination of polymer type compositions demonstrated marked similarity between the two fields indicating the sludge-applied field was a source of contamination for surrounding areas. Furthermore, MP was detected at a depth of 60-90 cm in the sludge-applied soil indicating that MP may also penetrate deep enough to reach agricultural drainage systems, although this effect is slight (1.6% of surface load). These results show that application of municipal sewage sludge on agricultural land can lead to further uncontrolled contamination, paving the way for future research to improve understanding of the extents of such effects on real farms to better inform future agricultural policy.

Deep Learning for Reconstructing Low-Quality FTIR and Raman Spectra─A Case Study in Microplastic Analyses.

Herein we report on a deep-learning method for the removal of instrumental noise and unwanted spectral artifacts in Fourier transform infrared (FTIR) or Raman spectra, especially in automated applications in which a large number of spectra have to be acquired within limited time. Automated batch workflows allowing only a few seconds per measurement, without the possibility of manually optimizing measurement parameters, often result in challenging and heterogeneous datasets. A prominent example of this problem is the automated spectroscopic measurement of particles in environmental samples regarding their content of microplastic (MP) particles. Effective spectral identification is hampered by low signal-to-noise ratios and baseline artifacts as, again, spectral post-processing and analysis must be performed in automated measurements, without adjusting specific parameters for each spectrum. We demonstrate the application of a simple autoencoding neural net for reconstruction of complex spectral distortions, such as high levels of noise, baseline bending, interferences, or distorted bands. Once trained on appropriate data, the network is able to remove all unwanted artifacts in a single pass without the need for tuning spectra-specific parameters and with high computational efficiency. Thus, it offers great potential for monitoring applications with a large number of spectra and limited analysis time with availability of representative data from already completed experiments.

High-Throughput Analyses of Microplastic Samples Using Fourier Transform Infrared and Raman Spectrometry.

Determining microplastics in environmental samples quickly and reliably is a challenging task. With a largely automated combination of optical particle analysis, Fourier transform infrared (FT-IR), and Raman microscopy along with spectral database search, particle sizes, particle size distributions, and the type of polymer including particle color can be determined. We present a self-developed, open-source software package for realizing a particle analysis approach with both Raman and FT-IR microspectroscopy. Our software GEPARD (Gepard Enabled PARticle Detection) allows for acquiring an optical image, then detects particles and uses this information to steer the spectroscopic measurement. This ultimately results in a multitude of possibilities for efficiently reviewing, correcting, and reporting all obtained results.

Entropy-Driven Selectivity for Chain Scission: Where Macromolecules Cleave.

We show that, all other conditions being equal, bond cleavage in the middle of molecules is entropically much more favored than bond cleavage at the end. Multiple experimental and theoretical approaches have been used to study the selectivity for bond cleavage or dissociation in the middle versus the end of both covalent and supramolecular adducts and the extensive implications for other fields of chemistry including, e.g., chain transfer, polymer degradation, and control agent addition are discussed. The observed effects, which are a consequence of the underlying entropic factors, were predicted on the basis of simple theoretical models and demonstrated via high-temperature (HT) NMR spectroscopy of self-assembled supramolecular diblock systems as well as temperature-dependent size-exclusion chromatography (TD SEC) of covalently bonded Diels-Alder step-growth polymers.

Quantitative Analysis of Step-Growth Polymers by Size Exclusion Chromatography.

We report an advanced analysis protocol that allows to quantitatively study the course of step-growth reactions by size exclusion chromatography on the example of the depolymerization of a Diels-Alder polymer based on a furane/maleimide couple at elevated temperatures. Frequently occurring issues of molar mass calibrations and overlap of monomer with solvent signals are addressed for determining reliable molar masses. Thereby, even kinetic parameters (e.g., rate coefficients) can be derived that otherwise would require performing additional spectroscopic experiments. Our results confirm first-order behavior of the rDA reaction with an activation energy of 33 kJ mol-1.

Entropy driven chain effects on ligation chemistry.

We report the investigation of fundamental entropic chain effects that enable the tuning of modular ligation chemistry - for example dynamic Diels-Alder (DA) reactions in materials applications - not only classically via the chemistry of the applied reaction sites, but also via the physical and steric properties of the molecules that are being joined. Having a substantial impact on the reaction equilibrium of the reversible ligation chemistry, these effects are important when transferring reactions from small molecule studies to larger or other entropically very dissimilar systems. The effects on the DA equilibrium and thus the temperature dependent degree of debonding (%debond) of different cyclopentadienyl (di-)functional poly(meth-)acrylate backbones (poly(methyl methacrylate), poly(iso-butyl methacrylate), poly(tert-butyl methacrylate), poly(iso-butyl acrylate), poly(n-butyl acrylate), poly(tert-butyl acrylate), poly(methyl acrylate) and poly(isobornyl acrylate)), linked via a difunctional cyanodithioester (CDTE) were examined via high temperature (HT) NMR spectroscopy as well as temperature dependent (TD) SEC measurements. A significant impact of not only chain mass and length with a difference in the degree of debonding of up to 30% for different lengths of macromonomers of the same polymer type but - remarkably - as well the chain stiffness with a difference in bonding degrees of nearly 20% for isomeric poly(butyl acrylates) is found. The results were predicted, reproduced and interpreted via quantum chemical calculations, leading to a better understanding of the underlying entropic principles.

State-of-the-art analytical methods for assessing dynamic bonding soft matter materials.

Dynamic bonding materials are of high interest in a variety of fields in material science. The reversible nature of certain reaction classes is frequently employed for introducing key material properties such as the capability to self-heal. In addition to the synthetic effort required for designing such materials, their analysis is a highly complex--yet important--endeavor. Herein, we critically review the current state of the art analytical methods and their application in the context of reversible bonding on demand soft matter material characterization for an in-depth performance assessment. The main analytical focus lies on the characterization at the molecular level.

Adaptable hetero Diels-Alder networks for fast self-healing under mild conditions.

A novel adaptable network based on the reversible hetero Diels-Alder reaction of a cyanodithioester and cyclopentadiene is presented. Reversible between 50-120 °C, the adjustable and self-healing features of the network are evidenced via temperature dependent rheology experiments and repetitive tensile tests whereas the network's chemical structure is explored by temperature dependent (1) H MAS-NMR spectroscopy.

Temperature-dependent size exclusion chromatography for the in situ investigation of dynamic bonding/debonding reactions.

Polymers capable of dynamic bonding/debonding reactions are of great interest in modern day research. Potential applications can be found in the fields of self-healing materials or printable networks. Since temperature is often used as a stimulus for triggering reversible bonding reactions, an analysis operating at elevated temperatures is very useful for the in situ investigation of the reaction mechanism, as unwanted side effects can be minimized when performing the analyses at the same temperature at which the reactions occur. A temperature-dependent size exclusion chromatographic system (TD SEC) has been optimized for investigating the kinetics of retro Diels-Alder-based depolymerization of Diels-Alder polymers. The changing molecular weight distribution of the analyzed polymers during depolymerization gives valuable quantitative information on the kinetics of the reactions. Adequate data interpretation methods were developed for the correct evaluation of the chromatograms. The results are confirmed by high-temperature dynamic light scattering, thermogravimetric analysis, and time-resolved nuclear magnetic resonance spectroscopy at high temperatures. In addition, the SEC system and column material stability under application conditions were assessed using thermoanalysis methods, infrared spectroscopy, nitrogen physisorption, and scanning electron microscopy. The findings demonstrate that the system is stable and, thus, we can reliably characterize such dynamically bonding/debonding systems with TD SEC.